Book Image

IBM SPSS Modeler Essentials

By : Jesus Salcedo, Keith McCormick
Book Image

IBM SPSS Modeler Essentials

By: Jesus Salcedo, Keith McCormick

Overview of this book

IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Derive – Nominal

One limitation of deriving a field as a flag is that you can only create a field with two outcomes. Deriving a field as a Nominal allows you to create a field with many categories. One of the most common uses of deriving a field as a Nominal is to transform a continuous field into a categorical field with multiple groups (for example, age groups, income groups, and so on).


It is common in data mining to create multiple versions of a field to see if one version is a better predictor in a model. If we wanted to create categories that were of equal width, equal size, or were based on standard deviations, the Binning node could be used instead.

In this example, we will modify the Age variable by classifying Age into a categorical variable called Age_Groups, which is the Age field banded into six groups, Young, Thirties, Forties, Fifties, Sixties, and Retired:

  1. Place a Derive node onto the canvas.
  2. Connect the Derive node named Employed to the new Derive node.
  3. Edit the new Derive...